Probabilistic linear discriminant analysis of i-vector posterior distributions
By Sandro Cumani, Oldrich Plchot, Pietro Laface. 2013
59 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tomi H. Kinnunen
- Douglas A. Reynolds
- Daniel Garcia-Romero
- Alan McCree
- Bing Jiang
- Xiangang Li
- Zhenyao Zhu
- Xuewei Zhang
In Experimental and Cognitive Psychology
In Signal Processing
- Tomi H. Kinnunen
- Douglas A. Reynolds
- Alan McCree
- Daniel Garcia-Romero
- Bing Jiang
- Xiangang Li
- Zhenyao Zhu
- Xuewei Zhang
Citations in the corpus
- Deep Speaker: an End-to-End Neural Speaker Embedding System (2017, methodology): “The traditional speaker recognition approach entails using i-vectors [3] and probabilistic linear discriminant analysis (PLDA) [5].”
- The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge (2014, background): “However, since the evaluation setup only provides access to i-vectors, available techniques to deal with duration variability (such as pi-vectors [11], or uncertainty propagation [12] [13]) are not applicable.”